Comparison between the types for the ages and genders

Questions

  • What are the differences between the type for the different combinations of ages and genders?
  • Do we observe the same changes as globally?

Loads

Libraries and functions

Warning message in is.na(x[[i]]):
“is.na() applied to non-(list or vector) of type 'environment'”Warning message in rsqlite_fetch(res@ptr, n = n):
“Don't need to call dbFetch() for statements, only for queries”
==========================================================================
*
*  Package WGCNA 1.63 loaded.
*
*    Important note: It appears that your system supports multi-threading,
*    but it is not enabled within WGCNA in R. 
*    To allow multi-threading within WGCNA with all available cores, use 
*
*          allowWGCNAThreads()
*
*    within R. Use disableWGCNAThreads() to disable threading if necessary.
*    Alternatively, set the following environment variable on your system:
*
*          ALLOW_WGCNA_THREADS=<number_of_processors>
*
*    for example 
*
*          ALLOW_WGCNA_THREADS=4
*
*    To set the environment variable in linux bash shell, type 
*
*           export ALLOW_WGCNA_THREADS=4
*
*     before running R. Other operating systems or shells will
*     have a similar command to achieve the same aim.
*
==========================================================================


Allowing multi-threading with up to 4 threads.
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."

Data

Stats

Wald padj < 0.05LFC > 0 (Wald padj < 0.05)LFC < 0 (Wald padj < 0.05)
GF VS SPF (F, 8w) 392192 200
GF VS SPF (M, 8w) 161 76 85
GF VS SPF (F, 52w) 738316 422
GF VS SPF (M, 52w) 393145 248
GF VS SPF (F, 104w)2038846 1192
GF VS SPF (M, 104w)2172898 1274

Differentially expressed genes

  GF VS SPF (F, 8w)   GF VS SPF (M, 8w)  GF VS SPF (F, 52w)  GF VS SPF (M, 52w) 
          0.5790816           0.6832298           0.4363144           0.6030534 
GF VS SPF (F, 104w) GF VS SPF (M, 104w) 
          0.5662414           0.5382136 

Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in stack.default(getgo(rownames(l$deg), "mm10", "geneSymbol")):
“non-vector elements will be ignored”Warning message in stack.default(getgo(rownames(as.data.frame(l$deg)), "mm10", "geneSymbol", :
“non-vector elements will be ignored”
Warning message:
“Removed 14812 rows containing non-finite values (stat_density).”

Comparison of the numbers per ages

Differentially expressed genes

DEG into gene co-expression network

  • White: up-regulated
  • Black: down-regulated
GF vs SPF 8w 52w 104w
F
M

GO analysis

Biological process

Dot-plot with the 20 most significant p-values for the different comparison

Using term, id as id variables
Using term, id as id variables

Network based on description similarity

GF vs SPF 8w 52w 104w
F
M

GF VS SPF (F, 8w)

<!DOCTYPE html>

GO Tree at "../results/dge/type-effect/type_gender_age/go/GF_VS_SPF_F_8w.png"

GF VS SPF (M, 8w)

<!DOCTYPE html>

GO Tree at "../results/dge/type-effect/type_gender_age/go/GF_VS_SPF_M_8w.png"

GF VS SPF (F, 52w)

<!DOCTYPE html>

GO Tree at "../results/dge/type-effect/type_gender_age/go/GF_VS_SPF_F_52w.png"

GF VS SPF (M, 52w)

<!DOCTYPE html>

GO Tree at "../results/dge/type-effect/type_gender_age/go/GF_VS_SPF_M_52w.png"

GF VS SPF (F, 104w)

<!DOCTYPE html>

GO Tree at "../results/dge/type-effect/type_gender_age/go/GF_VS_SPF_F_104w.png"

GF VS SPF (M, 104w)

<!DOCTYPE html>

GO Tree at "../results/dge/type-effect/type_gender_age/go/GF_VS_SPF_M_104w.png"

Cellular components

Dot-plot with the most over-represented CC GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

Molecular functions

Dot-plot with the most over-represented MF GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

KEGG pathways

Error in `$<-.data.frame`(`*tmp*`, labels, value = c("", "", "", "", "", : replacement has 33 rows, data has 37
Traceback:

1. plot_kegg_pathways(type_gender_age_deg$over_represented_KEGG[, 
 .     "category"], type_gender_age_deg$fc_deg, "../results/dge/type-effect/type_gender_age/kegg/over_repr_kegg/")
2. suppressMessages(pathview(gene.data = fc_deg, pathway.id = cat, 
 .     species = "Mus musculus", gene.idtype = "Symbol"))
3. withCallingHandlers(expr, message = function(c) invokeRestart("muffleMessage"))
4. pathview(gene.data = fc_deg, pathway.id = cat, species = "Mus musculus", 
 .     gene.idtype = "Symbol")
5. `$<-`(`*tmp*`, labels, value = c("", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", ""))
6. `$<-.data.frame`(`*tmp*`, labels, value = c("", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", ""))
7. stop(sprintf(ngettext(N, "replacement has %d row, data has %d", 
 .     "replacement has %d rows, data has %d"), N, nrows), domain = NA)

Pathway graphs available at ../results/dge/type-effect/type_gender_age/over_repr_kegg/

Pathway graphs available at ../results/dge/type-effect/type_gender_age/under_repr_kegg/

Comparison with Erny results

Protocol: 2 months old female mices (GF vs SPF)

Raw comparison of the results

Detailed comparison

  • Checking the correlation between the counts of Erny and our counts
  • Re-running a DGE analysis on the Erny's raw counts